Sup3rWind Data (CONUS)
This data contains paired European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit (WTK) images for 2007 and 2010 over two regions in the US, with domain sizes ~800x800 (latitudes from 25.89 to 41.58, and longitudes from -97.25 to -77.85) and ~600x1600 (latitudes from 32 to 46.68, and longitudes from -126.81 to -85.35), respectively. This is created using tools from the NREL-rex and NREL-sup3r packages (both linked as resources below). A Lambert projection is used for WTK. The data includes two variables: the u and v components of the wind velocity at 10m from the surface.
The training and validation splits consist of ERA5 at 30-km and WTK at 6-km spatial resolution from the year 2007. This 6-km WTK dataset is created by coarsening the WTK grid from its original 2-km resolution to 6-km resolution. The 30-km ERA5 is realigned, i.e. regrided to the 6-km WTK coarsened grid using inverse distance weighted interpolation. The year 2010 is used for testing, including two sets of test data with (1) ERA5 at 30-km and WTK at 6-km spatial resolution and (2) ERA5 at 30-km and WTK at 2-km spatial resolution. All of them have a temporal resolution of 1-hour. This data allows training machine learning models to downscale from low-resolution (LR) ERA5 to high-resolution (HR) WTK with an upsampling factor (the ratio of the size of the HR grid to the LR grid) of 5x and testing it on the same 5x factor as well as a higher upsampling factor of 15x. Please refer to the "Project Preprint" resource linked below for more details on the dataset and experiments. The work detailed in the "Sup3rWind Preprint" resource below also performs ERA5 to WTK downscaling.
Citation Formats
TY - DATA
AB - This data contains paired European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit (WTK) images for 2007 and 2010 over two regions in the US, with domain sizes ~800x800 (latitudes from 25.89 to 41.58, and longitudes from -97.25 to -77.85) and ~600x1600 (latitudes from 32 to 46.68, and longitudes from -126.81 to -85.35), respectively. This is created using tools from the NREL-rex and NREL-sup3r packages (both linked as resources below). A Lambert projection is used for WTK. The data includes two variables: the u and v components of the wind velocity at 10m from the surface.
The training and validation splits consist of ERA5 at 30-km and WTK at 6-km spatial resolution from the year 2007. This 6-km WTK dataset is created by coarsening the WTK grid from its original 2-km resolution to 6-km resolution. The 30-km ERA5 is realigned, i.e. regrided to the 6-km WTK coarsened grid using inverse distance weighted interpolation. The year 2010 is used for testing, including two sets of test data with (1) ERA5 at 30-km and WTK at 6-km spatial resolution and (2) ERA5 at 30-km and WTK at 2-km spatial resolution. All of them have a temporal resolution of 1-hour. This data allows training machine learning models to downscale from low-resolution (LR) ERA5 to high-resolution (HR) WTK with an upsampling factor (the ratio of the size of the HR grid to the LR grid) of 5x and testing it on the same 5x factor as well as a higher upsampling factor of 15x. Please refer to the "Project Preprint" resource linked below for more details on the dataset and experiments. The work detailed in the "Sup3rWind Preprint" resource below also performs ERA5 to WTK downscaling.
AU - Sinha, Saumya
A2 - Benton, Brandon
A3 - Emami, Patrick
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO -
KW - downscaling
KW - weather
KW - wind
KW - energy
KW - climate
KW - super-resolution
KW - windspeed
KW - neural operators
KW - deep learning
KW - CONUS
KW - sup3rwind
KW - wtk
KW - model
KW - machine learning
KW - United States
KW - Computational Science
KW - data
KW - dataset
KW - processed data
LA - English
DA - 2024/07/16
PY - 2024
PB - National Renewable Energy Laboratory (NREL)
T1 - Sup3rWind Data (CONUS)
UR - https://data.openei.org/submissions/6210
ER -
Sinha, Saumya, et al. Sup3rWind Data (CONUS). National Renewable Energy Laboratory (NREL), 16 July, 2024, Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/6210.
Sinha, S., Benton, B., & Emami, P. (2024). Sup3rWind Data (CONUS). [Data set]. Open Energy Data Initiative (OEDI). National Renewable Energy Laboratory (NREL). https://data.openei.org/submissions/6210
Sinha, Saumya, Brandon Benton, and Patrick Emami. Sup3rWind Data (CONUS). National Renewable Energy Laboratory (NREL), July, 16, 2024. Distributed by Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/6210
@misc{OEDI_Dataset_6210,
title = {Sup3rWind Data (CONUS)},
author = {Sinha, Saumya and Benton, Brandon and Emami, Patrick},
abstractNote = {This data contains paired European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit (WTK) images for 2007 and 2010 over two regions in the US, with domain sizes ~800x800 (latitudes from 25.89 to 41.58, and longitudes from -97.25 to -77.85) and ~600x1600 (latitudes from 32 to 46.68, and longitudes from -126.81 to -85.35), respectively. This is created using tools from the NREL-rex and NREL-sup3r packages (both linked as resources below). A Lambert projection is used for WTK. The data includes two variables: the u and v components of the wind velocity at 10m from the surface.
The training and validation splits consist of ERA5 at 30-km and WTK at 6-km spatial resolution from the year 2007. This 6-km WTK dataset is created by coarsening the WTK grid from its original 2-km resolution to 6-km resolution. The 30-km ERA5 is realigned, i.e. regrided to the 6-km WTK coarsened grid using inverse distance weighted interpolation. The year 2010 is used for testing, including two sets of test data with (1) ERA5 at 30-km and WTK at 6-km spatial resolution and (2) ERA5 at 30-km and WTK at 2-km spatial resolution. All of them have a temporal resolution of 1-hour. This data allows training machine learning models to downscale from low-resolution (LR) ERA5 to high-resolution (HR) WTK with an upsampling factor (the ratio of the size of the HR grid to the LR grid) of 5x and testing it on the same 5x factor as well as a higher upsampling factor of 15x. Please refer to the "Project Preprint" resource linked below for more details on the dataset and experiments. The work detailed in the "Sup3rWind Preprint" resource below also performs ERA5 to WTK downscaling.},
url = {https://data.openei.org/submissions/6210},
year = {2024},
howpublished = {Open Energy Data Initiative (OEDI), National Renewable Energy Laboratory (NREL), https://data.openei.org/submissions/6210},
note = {Accessed: 2025-04-24}
}
Details
Data from Jul 16, 2024
Last updated Feb 17, 2025
Submitted Oct 10, 2024
Organization
National Renewable Energy Laboratory (NREL)
Contact
Saumya Sinha
303.384.6764